Multiobjective trajectory optimization with a cutting and padding encoding strategy for single-UAV-assisted mobile edge computing system

Abstract

Mobile edge computing (MEC) is used to provide IT services environment and cloud computing capabilities at the edge of the network. As the technology of unmanned aerial vehicles (UAVs) matures, the growing attempts have been made to use UAVs to replace fixed ground stations for MEC due to their flexibility. In this work, we study the multiobjective trajectory optimization for mobile edge computing system assisted by a single UAV, where the UAV is used to provide computing services for Internet of Things (IoT) devices located on the ground. A multiobjective trajectory optimization problem is formulated, which not only needs to minimize the energy consumption of the MEC system to provide computing services to all IoT devices, but also minimize the task urgency indicator by optimizing the UAV’s flight trajectory. In this problem, the number and the locations of hover points (HPs) of UAVs have been taken into consideration. To solve this problem, a multiobjective trajectory optimization algorithm with a cutting and padding encoding strategy is proposed, where the cutting and padding encoding strategy is used to help optimize the population whose individuals may have different lengths. The verification experiments are carried out on a set of instances with up to 400 IoT devices and the experimental results demonstrate the promising performance of the proposed algorithm for trajectory optimization problems in a single-UAV-assisted MEC system.